Developing problem-solving competency is central to Science, Technology, Engineering, and Mathematics (STEM) education, yet translating this priority into effective approaches to problem-solving instruction and assessment has been a significant challenge. The recent proliferation of generative artificial intelligence (genAI) tools like ChatGPT in higher education introduces new considerations: how to define problem-solving competency in a genAI era, and how these tools can help or hinder students' development of STEM problem-solving competency. Our research takes steps in examining these considerations by studying how and why college students are currently using genAI tools in their STEM coursework, with a specific focus on how they employ these tools to support their problem-solving. We conducted an online survey of 40 STEM college students from diverse institutions across the US. In addition, we surveyed 28 STEM faculty to understand instructor views on effective and ineffective genAI tool use in STEM courses and their guidance for students. Our findings reveal high adoption rates and diverse applications of genAI tools among STEM students. The most common use cases of genAI tools in STEM coursework include finding explanations, exploring related topics, summarizing readings, and helping with problem-set questions. The primary motivation for using genAI tools in STEM coursework was to save time. Moreover, we found that over half of the student participants reported simply inputting a problem for AI to generate solutions, potentially bypassing their own problem-solving processes. These findings indicate that despite high adoption rates, students' current approaches to utilizing genAI tools often fall short in enhancing their own STEM problem-solving competencies. The study also explored students' and STEM instructors' perceptions of the benefits and risks associated with using genAI tools in STEM education. Our findings provide insights into how to guide students on appropriate genAI use in STEM courses and how to design genAI-based tools to foster students' problem-solving competency.

Digital games offer promising platforms for assessing student higher-order competencies such as problem-solving. However, processing and analyzing the large volume of interaction log data generated in these platforms to uncover meaningful behavioral patterns remain a complex research challenge. In this study, we employ sequence mining and clustering techniques to examine students’ log data in an interactive puzzle game that requires player to change rules to win the game. Our goal is to identify behavioral characteristics associated with the problem-solving practices adopted by individual students. The findings indicate that the most effective problem solvers made fewer rule changes and took longer time to make those changes across both an introductory and a more advanced level of the game. Conversely, rapid rule change actions were linked to ineffective problem-solving. This research underscores the potential of sequence mining and cluster analysis as generalizable methods for understanding student higher-order competencies through log data in digital gaming and learning environments. It also suggests future directions on how to provide just-in-time, in-game feedback to enhance student problem-solving competences.

Interactive simulations provide an exciting opportunity to assess and teach students the practices used by scientists and engineers to solve real-world problems. This study examines how the logged interaction data from a simulation-based task could be used to automate the assessment of complex problem-solving practices. A total of 73 college students worked on an interactive circuit puzzle embedded in a science simulation in an interview setting. Their problem-solving processes were videotaped and logged in the backend of the simulation. We extracted different sets of features from the log data and evaluated their effectiveness as predictors of students' problem-solving success and evidence for specific problem-solving practices. Our results indicate that the application of data mining techniques guided by knowledge gained from qualitative observation was instrumental in the discovery of semantically meaningful features from the raw log data. These knowledge-grounded features were significant predictors of students' overall problem-solving success and provided evidence on how well they adopted specific problem-solving practices, including decomposition, data collection, and data recording. The results point to promising directions for how scaffolding/feedback could be provided in educational simulations to enhance student learning in problem-solving skills.

Teaching students to become skillful problem solvers is a goal of university education, but it has been difficult to measure such skill or demonstrate the benefits of particular educational experiences. This paper presents a study of college students solving a problem unrelated to their academic majors. The analysis suggests that the educational experiences of Computer Science (CS) students may better train them in problem-solving than the experiences of other majors. In this study, students from a variety of undergraduate majors and grade levels were given a 15-minute problem-solving task embedded in an interactive science simulation. The complex task calls upon many problem-solving practices needed by scientists and engineers in their professions. Although this task has little resemblance to the problems encountered in a computer science course, CS students performed significantly better than students in any other major. In addition, only for CS students was there an indication of improvement in problem-solving from lower to upper grade levels. We propose that general problem-solving and computational thinking share some common practices, such as problem decomposition and comprehensive data collection. Furthermore, we present preliminary evidence that training in computational thinking is transferable to problem-solving tasks across domains and discuss how the unique features of CS programming assignments could be generalized to other science and engineering courses to foster students' general problem-solving skills.

In this paper we will examine students' meta-modeling knowledge in the context of their participation in a Bifocal Modeling activity. Bifocal Modeling is an inquiry-based approach for science learning, which incorporates both physical experimentation and virtual modeling. The current study combines three separate case studies of students participating in different implementation modes of the Bifocal Modeling process. Different implementation methods require different modeling practices, and we will examine the consequences of these practices for students' meta-modeling knowledge. The concern of our investigation will be the ways that students critically evaluate scientific models and their understanding of the limitations of those models. Data suggest that model construction (as opposed to simple interaction) lead to deeper meta-modeling knowledge.

In this paper, we describe a set of user studies within the Bifocal Modeling (BM) framework. BM juxtaposes physical and computer models using sensor-based and computer modeling technologies, highlighting the discrepancies between ideal and real systems. When creating bifocal models, students build both a physical model with sensors of a given scientific phenomenon, and a computer model of the same phenomenon, connecting the two in real time with a special hardware interface. In this paper, we describe four formats for using BM in the classroom, as well as its affordances and characteristics.

This paper introduces Process Pad, an interactive, low-cost multi-touch tabletop platform designed to capture students' thought process and facilitate their explanations. Process Pad is designed to help students improve their thinking skills and meta-cognition in various subjects. The system is intended to dynamically externalize how a student arrives at the final answer. Process Pad enables the documentation of students' think-aloud narratives that would otherwise be tacit. Our focus is on identifying and understanding key themes in creating opportunities for students to externalize and represent their thought process using multimodal data. From our user observations, we gleaned four design perspectives as essential criteria based upon which we form our design decisions: flexibility, tangibility, collaboration and affordability. Our initial results show that for many users explaining their reasoning or problem-solving procedure is a challenging activity in itself, and for learners to be able to deepen their understanding by narrating or re-enacting a process there would be many intervening steps. To address these challenges we designed scaffolding activities, which made use of the system's affordances to improve students' explanation skills.